Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

TL;DR

This paper introduces a novel approach to multiagent planning by exploiting anonymity and frame-action hypergraphs, significantly improving scalability in large agent populations within the I-POMDP framework.

Contribution

It extends I-POMDPs to incorporate anonymity and context-specific independence, enabling efficient planning in environments with over 1,000 agents.

Findings

Achieved computational gains by exploiting problem structures.

Successfully solved a multiagent problem with over 1,000 agents.

Demonstrated improved scalability in multiagent planning.

Abstract

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.